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- Joaquín J. Torres, M. A. Muñoz, Joaquín Marro, P. L. Garrido
- Neurocomputing
- 2004

We studied the computational properties of an attractor neural network (ANN) with different network topologies. Though fully connected neural networks exhibit, in general, a good performance, they are biologically unrealistic, as it is unlikely that natural evolution leads to such a large connectivity. We demonstrate that, at finite temperature, the… (More)

- Joaquín J. Torres, Jesús M. Cortés, Joaquín Marro, Hilbert J. Kappen
- Neural Computation
- 2007

We study the effect of competition between short-term synaptic depression and facilitation on the dynamic properties of attractor neural networks, using Monte Carlo simulation and a mean-field analysis. Depending on the balance of depression, facilitation, and the underlying noise, the network displays different behaviors, including associative memory and… (More)

- Jesús M. Cortés, Joaquín J. Torres, Joaquín Marro, P. L. Garrido, Hilbert J. Kappen
- Neural Computation
- 2006

We study both analytically and numerically the effect of presynaptic noise on the transmission of information in attractor neural networks. The noise occurs on a very short timescale compared to that for the neuron dynamics and it produces short-time synaptic depression. This is inspired in recent neurobiological findings that show that synaptic strength… (More)

- J. Marro
- 2008

We present exact results, as well as some illustrative Monte Carlo simulations, concerning a stochastic network with weighted connections in which the fraction of nodes that are dynamically synchronized, ρ ∈ [0, 1] , is a parameter. This allows one to describe from single–node kinetics (ρ → 0) to simultaneous updating of all the variables at each time unit… (More)

- Samuel Johnson, Joaquín J Torres, J Marro, Miguel A Muñoz
- Physical review letters
- 2010

Why are most empirical networks, with the prominent exception of social ones, generically degree-degree anticorrelated? To answer this long-standing question, we define the ensemble of correlated networks and obtain the associated Shannon entropy. Maximum entropy can correspond to either assortative (correlated) or disassortative (anticorrelated)… (More)

- Joaquín Marro, Joaquín J. Torres, Jesús M. Cortés
- Neural Networks
- 2007

We present a neurobiologically-inspired stochastic cellular automaton whose state jumps with time between the attractors corresponding to a series of stored patterns. The jumping varies from regular to chaotic as the model parameters are modified. The resulting irregular behavior, which mimics the state of attention in which a system shows a great… (More)

- Joaquín J. Torres, J. Marro
- Scientific reports
- 2015

We here illustrate how a well-founded study of the brain may originate in assuming analogies with phase-transition phenomena. Analyzing to what extent a weak signal endures in noisy environments, we identify the underlying mechanisms, and it results a description of how the excitability associated to (non-equilibrium) phase changes and criticality optimizes… (More)

- J J Torres, P L Garrido, J Marro
- 1997

We study a kinetic neural network in which the intensity of synaptic couplings varies on a timescale of order p(1 − p) −1 compared with that for neuron variations. We describe some exact and mean-field results for p → 0. This includes, for example, the Hopfield model with random fluctuations of synapse intensities such that neurons couple each other, on… (More)

- Samuel Johnson, J. Marro, Joaquín J. Torres
- PloS one
- 2013

Short-term memory in the brain cannot in general be explained the way long-term memory can--as a gradual modification of synaptic weights--since it takes place too quickly. Theories based on some form of cellular bistability, however, do not seem able to account for the fact that noisy neurons can collectively store information in a robust manner. We show… (More)

- J. M. Cortes, P. L. Garrido, +4 authors J. J. Torres
- 2004

This talk reports on a series of efforts during the last decade aimed at modeling in a computer the cooperative properties that, according to some experimental evidence, could be relevant for the processing of patterns in a brain. In particular, a main recent interest is in designing fast and reliable algorithms for " restoring " a pattern, namely,… (More)